Reinforcement learning in the presence of rare events
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Learning agents often find themselves in environments in which rare significant events occur independently of their current choice of action. Traditional reinforcement learning algorithms sample events according to their natural probability of occurring, and therefore tend to exhibit slow convergence and high variance in such environments. In this thesis, we assume that learning is done in a simulated environment in which the probability of these rare events can be artificially altered. We present novel algorithms for both policy evaluation and control, using both tabular and function approximation representations of the value function. These algorithms automatically tune the rare event probabilities to minimize the variance and use importance sampling to correct for changes in the dynamics. We prove that these algorithms converge, provide an analysis of their bias and variance, and demonstrate their utility in a number of domains, including a large network planning task.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it